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the lateral linear quadratic regulator algorithm for autonomous driving in carsim/simulink
ROS implementation of an mpc controller for Aion R6 Rover- Final Year Project: Optimal Path Planning and Following for Race Cars
Python implementation of an automatic parallel parking system in a virtual environment, including path planning, path tracking, and parallel parking
The summer project of SDU by Hu Shurui, Wang Yunping, He Lijie, Zhu Junyi, Hou Wei
Model Predictive Controller for Autonomous Driving implemented using ROS and C++
Kinematic MPC and dynamic LPV-LQR state feedback control for an autonomous vehicle
Open-source software for self-driving vehicles
This is the source code of the feasibility study for Autoware architecture proposal.
Main branch for BARC related code
和bilibili教学视频相关的一些源代码
Implementation of the CNN from End to End Learning for Self-Driving Cars on a Nvidia Jetson TX1 using Tensorflow and ROS
A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.
Source code for drone programming course
A few simple tutorials for dynamical systems and control. Most require Matlab.
Repo for the paper 'Forecasting Pedestrian Trajectory with Machine-Annotated Training Data'. IV 2019
MPC control for vehicle
To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles
The repo develops a general and extensible RL environment for large-scale autonomous driving tasks.
广东工业大学 LaTeX 论文模板
Common packages for the Clearpath Husky
Hybrid A Star algorithm C++ implementation
This is a global planner plugin of ROS move_base package
1/20 MiniCar: An ackermann based rover for MPC and Pure-Pursuit controller
Implement an MPC approach to coordinate automated vehicles with fixed priorities
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.